If a frequency band has primary and secondary users, then the cognitive radios of the secondary users must monitor the band and be prepared to cease their transmissions if a primary user's radio begins to transmit. Traditional spectrum sensing requires the secondary radios to refrain from communicating while they check for the emergence of primary signals. We propose and evaluate methods by which the secondary radios can continue their communications while simultaneously monitoring the band to detect any transmissions that are initiated by the primary radios. Our methods for spectrum monitoring supplement traditional spectrum sensing and improve the communications efficiency of the secondary radios.

New distributed protocols for channel access and adaptive spreading are described and evaluated for use in direct-sequence spread-spectrum packet radio networks that have no central controllers, access points, or base stations. The protocols rely on demodulator statistics to provide the information needed for efficient sharing of the frequency band and compensation for changes in the communications environment in a network of half-duplex radios for which simultaneous two-way communications is not possible.

Adaptation of the code rate and spreading factor for direct-sequence spread-spectrum packet radio transmissions provides much higher throughput than if the code rate and spreading factor are fixed. A low-complexity protocol for packet-by-packet adaptation can be obtained if receiver statistics that are developed during the demodulation of a packet are used to choose the code rate and spreading factor for the next packet. The design and evaluation of the protocol typically requires extensive simulations, especially for modern packet radio receivers that employ iterative decoding. We show that analytical methods based on Shannon capacity results greatly simplify the design of the adaptive protocol and provide good estimates of its throughput performance.

Next generation packet radio networks will have far greater processing capabilities than current radio systems. We propose and evaluate decoding techniques that make use of such capabilities to increase the probability of successful decoding. We propose a metric derived from statistics collected during demodulation in a binary CDMA receiver. We investigate several methods to apply the proposed metric to the demodulator's soft-decision outputs prior to decoding. Our soft-decision decoding techniques are designed to mitigate the effects of interference from other signals in the frequency band. We compare the performance of our proposed metric to the log-likelihood ratio (LLR) metric, which requires that the mean signal level and noise variance are known for each bit position. Rather than attempt to estimate these parameters directly, our metric uses demodulator statistics and thus does not require pilot symbols or training sequences typically required by an LLR-based metric.

In many dynamic spectrum access networks, it is necessary for secondary users to monitor the frequency band in which they are communicating so that they can determine if the primary user has begun transmission. Traditional sensing methods require the transmitters of the secondary users to be silent while spectrum monitoring is performed. Statistics that are derived easily in a communications receiver have the potential to permit a level of spectrum monitoring while the receiver is demodulating and decoding a packet, so that it is not necessary to silence the secondary transmitters. The proposed techniques for spectrum monitoring can supplement existing methods and reduce the amount of time that secondary users must refrain from communicating.